Employee engagement in Indian power companies: study of executives identifies four key drivers

Purpose The purpose was to study how to create employee engagement in the Indian power sector. It also explored the antecedents of employee engagement. Design/methodology/approach Responses were gathered from executives in three companies working at power plants, distribution and transmission offices spread across five districts in the states of Odisha and West Bengal. Findings The research revealed how co-worker trust, supervisor trust and organizational trust all mediate the relationship between organizational culture and employee engagement. The study also compares engagement levels of employees at the three companies. Originality/value The authors say it is crucial to enhance employee engagement by identifying sector-specific factors. The results also help policymakers to appreciate the impact of organizational culture on employee engagement, and formulate appropriate policies.

Turning Around the Power Distribution Sector: Learnings and Best Practices from Reforms

The distribution sector has been the Achilles’ heel of the Indian power sector, consistently making large losses, reflecting weaknesses in operations, infrastructure, and regulation. We will not be able to achieve a high-growth, low-carbon economy unless the distribution sector achieves profitability.Different states in India have followed different reform trajectories, and today, policy-makers can draw upon a wealth of accumulated experience. This report aims to document the best practices and lessons from across India, and where required, across the world.

DMAPT: Study of Data Mining and Machine Learning Techniques in Advanced Persistent Threat Attribution and Detection

Modern-day malware is intelligent enough to hide its presence and perform stealthy operations in the background. Advance Persistent Threat (APT) is one such kind of malware attack on sensitive corporate and banking networks to stay there for a long time undetected. In real-time corporate networks, identifying the presence of intruders is a big challenging task for security experts. Recent APT attacks like Carbanak, The Big Bang, and Red Echo attack (targeting the Indian power sector) are ringing alarms globally. New data exfiltration methods and advancements in malware techniques are the two main reasons for rapid and robust APT evolution. Although many traditional and hybrid methods are available to detect this stealthy malware, the number of target-specific attacks are increasing rapidly at global level. Attackers have been crafting payloads resistant to malware sandbox environments so that traditional sandboxing techniques may not work with these APT malware detection. In this paper, we shed light on various Data Mining, Machine Learning techniques and frameworks used in both Attribution and Detection of APT malware. Added to this, our work highlight GAP analysis and need for paradigm shift in existing techniques to deal with evolving modern APT malware.

The dynamics of Indian energy mix: a two-phase analysis

PurposeThe Indian power sector is dominated by coal. Environmental awareness and advances in techno-economic front have led to a slow but steady shift towards greener alternatives. The distributions of both fossil fuel resources and renewable energy potential are not uniform across the states. Paper attempts to answer how the states are performing in the sector and how the renewable energy and conventional resources are affecting the dynamics.Design/methodology/approachThe authors employ a two-stage data envelopment analysis (DEA) to rank the performance of Indian states in the power sector. Multi-stage analysis opens up the DEA black-box through disaggregating power sector in two logical sub-sectors. The performance is evaluated from the point-of-view of policy formulating and implementing agencies. Further, an econometric analysis using seemingly unrelated regression equations (SURE) is conducted to estimate the determinants of total and industrial per-capita electricity consumption.FindingsEfficiency scores obtained from the first phase of analysis happens to be a significant explanatory variable for power consumption. The growth in electricity consumption, which is necessary for economic wellbeing, is positively affected by both renewable and non-renewable sources; but conventional sources have a larger impact on per-capita consumption. Yet, the share of renewables in the energy mix has positive elasticity. Hence, the findings are encouraging, because development in storage technologies, falling costs and policy interventions are poised to give further impetus to renewable sources.Originality/valueThe study is one of the very few where entire spectrum of the Indian power sector is evaluated from efficiency perspective. Further, the second phase analysis gives additional relevant insights on the sector.

Particle Swarm Optimization Based Wheeling Price Calculation for IEEE 14 Bus System by Using MVA-MILE And MVA-MILE Approaches

In the deregulated power system, it would be difficult to assess and evaluate the prices for transmission lines in the power factor-based approach. In the Indian power sector, different electrical power users follow a regional price method of wheeling power rates. Because of the policy change, the postage stamp system is no longer suitable for the Indian electricity generation. It is because nonlinear power flow is very influential in creating prices between transmission lines. The MW-mile method is used for real power wheeling price assessment, and MVA-mile method is employed for the accurate and reactive power wheeling price assessment. For various flow-based processes to be efficient, decision-makers should consider the different optimization techniques. This paper proposes a new method by which wheeling prices can be allocated for the Indian utility IEEE 14-bus system using the MVA-mile and MW-mile method based on the PSO algorithm approach for optimum power flow calculation In this paper, MW-Mile based, MVA-Mile based incremental cost of power demand addition and power factor-based approach are utilised. Sensitivity analysis will take place. It is also pertinent to study the DC load flow based costs.

Employer brand to leverage employees’ intention to stay through sequential mediation model: evidence from Indian power sector

Purpose This study aims to explain the linkage between employer branding and employee retention; a sequential mediation is hypothesized, where it is proposed that the relationship between employer branding and employee retention is sequentially mediated by person–organization fit (P-O fit) and organizational identification. Design/methodology/approach The sample belongs to 224 executive-level employees of the Indian power sector organization. The sequential mediation model is tested by using SPSS macro command of Preacher and Hayes. Findings The findings established that the relationship between employer brand and employees’ intention to stay is sequentially mediated by P-O fit and organizational identification. Practical implications The findings emphasize the role of employer brand on constructs such as P-O fit, organizational identification and intention to stay. In addition, the established mechanism emphasizes the role of P-O fit to realize the benefits such as organizational identification and employee retention. Originality/value Internal branding efforts may have a major impact on workforce attitude and behavior including engagement, job performance and retention. “Yet studies of the positive impact of employer brand on employee attitudes and behaviors, or of the factors that shape employer brand, are rare” (Charbonnier-Voirin et al., 2017, p. 2). Along the line of such gap, this study has taken up to test the unexplored sequential mediation mechanism between employer brand and employees’ intention to stay through P-O fit and organizational identification.